This setup has been very easy to work with and very productive. Bjarne Stroustrup the creator of C++ gave an interview about the new features in the C++0x standard and TR1. C++ now has a lot of innovating programming constructs e.g. template meta programming, lambda functions, concepts and traits. When I found out that "axiom" is going to be a keyword in C++ my inner mathematician demanded that I take a second look at C++ in connection with computer vision. This post is a review of my personal past experience with computer vision in C++ and Java.
Java - Which langauge should i use for Artificial intelligence on web projects. Computers: Artificial Intelligence: Machine Learning: Software. Artificial Intelligence Chat Bot Programming And Tutorial.

" "I think it's the most well-designed ML package I've seen so far.
" "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...].
"
C++ - machine-learning, artificial-intelligence and computational-linguistics. Java and AI-programming - DaemonForums. I would have to second the Perl / Python bit; they are both excellent languages for dealing with the Internet and processing data.

Perl is arguably one of the most expressive languages ever made (if there is anything more expressive for general programming, it would probably have to be ASM or LISP). Python can often be used to produce maintainable code that also gets the job done painlessly, although the maintainability part depends on the monkey. Perl is however a huge language syntactically, and Python has many standard modules to choose from.
[Resource] Learn AI programming in Ruby. Machine Learning in Games Development. In this article, I shall outline the current perceptions of 'Machine Learning' in the games industry, some of the techniques and implementations used in current and future games, and then explain how to go about designing your very own 'Learning Agent'.

The Games Industry Machine Learning has been greeted with a certain amount of caution by games developers, and until recently, has not been used in any major games releases. Why is this -- surely there must be potential demand for games that can learn -- games that can adjust strategy to adapt to different opponents? There are several major reasons for the lack of enthusiasm which has, for a long time, been exhibited. Another question to be asked, is just how important is it for a game to 'learn'? Many games companies are currently looking at the possibility of making games that can match the player's ability by altering tactics and strategy, rather than by improving the ability of opponents. Varieties of Learning Creating a Learning Agent. AI on the Web. This page links to 820 pages around the web with information on Artificial Intelligence.

Links in Bold* followed by a star are especially useful and interesting sites. Links with a + sign at the end have "tooltip" information that will pop up if you put your mouse over the link for a second or two. If you have new links to add, mail them to peter@norvig.com. We hope you can find what you want in one of the following subtopics:
Online Code Repository. The goal is to have working code for all the algorithms in the book in a variety of languages.

So far, we have Java, Lisp and Python versions of most of the algorithms. There is also some old code in C++, C# and Prolog, but these are not being maintained. We also have a directory full of data files. Let peter@norvig.com know what languages you'd like to see, and if you're willing to help.
Aiqus: the ai & cs learning community. Khan Academy. AI-Class: "Introduction to Artificial Intelligence"
Want to learn Artificial Intelligence? Good.
Take Stanford's AI Course For Free Online. Not too long ago we told you about how you can access the course materials for Stanford University's introduction to computer science course.

If you're looking for something a bit more advanced, Stanford will offer its artificial intelligence class online for free this fall. It will run from Sept 26 - Dec 16. Online enrollment ends Sept 10. The course will be taught by Sebastian Thrun and Peter Norvig. The course will include online lectures by the two, and according to the course website both professors will be available for online discussions. Thrun is a Research Professor of Computer Science at Stanford, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Norvig is Director of Research at Google, and the co-author of the course's primary text Artificial Intelligence: A Modern Approach.
DARPA Grand Challenge (2005)
The second driverless car competition of the DARPA Grand Challenge was a 212 km (132 mi) off-road course that began at 6:40am on October 8, 2005, near the California/Nevada state line.

All but one of the 23 finalists in the 2005 race surpassed the 11.78 km (7.32 mi) distance completed by the best vehicle in the 2004 race. Five vehicles successfully completed the course: Beer Bottle Pass Vehicles in the 2005 race passed through three narrow tunnels and navigated more than 100 sharp left and right turns. The race concluded through Beer Bottle Pass, a winding mountain pass with sheer drop-offs on both sides. The natural rivalry between the teams from Stanford and Carnegie Mellon (Sebastian Thrun, head of the Stanford team was previously a faculty member at Carnegie Mellon and colleague of Red Whittaker, head of the CMU team) was played out during the race.

You (YOU!) Can Take Stanford's 'Intro to AI' Course Next Quarter, For Free. Stanford has been offering portions of its robotics coursework online for a few years now, but professors Sebastian Thrun and Peter Norvig are kicking things up a notch (okay, lots of notches) with next semester's CS221: Introduction to Artificial Intelligence. For the first time, you can take this course, along with several hundred Stanford undergrads, without having to fill out an application, pay tuition, or live in a dorm.

This is more than just downloading materials and following along with a live stream; you're actually going to have to do all the same work as the Stanford students. There's a book you'll need to get. There will be at least 10 hours per week of studying, along with weekly graded homework assignments. The professors will be available to answer your questions.
To "learn AI"
First learn basic mathematics. I keep getting asked this question and I keep saying the same thing - to three people in the last week, for e.g, two of whom were working through (or planning to work through) AIMA - so I thought I'd put this down here (and point anyone who asks the same question to this entry in the future).

Learning AI (or any deep comp.sci for that matter) is not like learning J2EE or ruby "dsl"s or whatever the fad du jour in the enterprise software world is - read a few chapters of the latest bestselling "pragmatic" book, write some crappy web site and hey presto you are the expert. "Real" comp sci doesn't quite work like that. To understand a standard 3 layer feed forward neural network, for example, you need to have a solid grip on And, a feed forward neural network is only one type of pattern recognizer (or function approximator). If one is willing to work hard, there are very few fields as fascinating as the various branches of AI.

Artificial Intelligence. Artificial Intelligence Depot. Open Source Project Aims to Create Human Level Artificial Intelligence. The Short: Researches across the globe are making daily advances towards the development of human level artificial intelligence, but sadly the algorithms and the software that represent these advances often remain hidden within researcher’s computer labs, out of reach for others to review and build upon.

Enter OpenCog, an entire website dedicated to the development and distribution of artificial intelligence tools, software, and resources that are open source and freely available for anyone to use and modify. Opencog’s ultimate goal is to develop true human level artificial intelligence, but even if Opencog fails to achieve this most ambitious of goals the project still seems poised to bring many other significant benefits to the AI community. Opencog was only recently launched in 2008 and at present appears to have a small following. The Long:
Python - pythoncad - [Chicago] An open source AI research project. Api - Open Source AI Bot interfaces. Open Source AI Code for Mod Developers?
Open source artificial intelligence project. SparkleShare – Un clone de Dropbox open source qui fonctionne !